{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to classification\n",
    "\n",
    "We will introduce the task of classification, and look at a naive way of doing it using a method called [k-Nearest Neighbors](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\n",
    "\n",
    "Classification is one of supervised machine learning's most basic tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification\n",
    "\n",
    "Consider the following small dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "<tr><td>class</td><td>Humidity (%)</td><td>Pressure (kPa)</td></tr>\n",
       "<tr><td>Sun  </td><td>29.0        </td><td>101.7         </td></tr>\n",
       "<tr><td>Rain </td><td>60.0        </td><td>98.6          </td></tr>\n",
       "<tr><td>Sun  </td><td>40.0        </td><td>101.1         </td></tr>\n",
       "<tr><td>Rain </td><td>62.0        </td><td>99.9          </td></tr>\n",
       "<tr><td>Sun  </td><td>39.0        </td><td>103.2         </td></tr>\n",
       "<tr><td>Rain </td><td>51.0        </td><td>97.6          </td></tr>\n",
       "<tr><td>Sun  </td><td>46.0        </td><td>102.1         </td></tr>\n",
       "<tr><td>Rain </td><td>55.0        </td><td>100.2         </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from IPython.display import HTML, display\n",
    "import tabulate\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# toy datast of whether or not it will be rainy or sunny\n",
    "feature_names = [\"Humidity (%)\", \"Pressure (kPa)\"]\n",
    "data = [[29, 101.7], [60, 98.6], [40, 101.1], [62, 99.9], [39, 103.2], [51, 97.6], [46, 102.1], [55, 100.2]]\n",
    "labels = [\"Sun\",\"Rain\",\"Sun\",\"Rain\",\"Sun\",\"Rain\",\"Sun\",\"Rain\"]\n",
    "\n",
    "# display table\n",
    "table_labels = np.array(['class']+feature_names).reshape((1, 1+len(feature_names)))\n",
    "table_data = np.concatenate([np.array(labels).reshape(len(data), 1), data], axis=1)\n",
    "table_full = np.concatenate([table_labels, table_data], axis=0)\n",
    "display(HTML(tabulate.tabulate(table_full, tablefmt='html')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these points on a scatterplot. In the following, a `+` means \"Rain\" and a `-` is \"Sun\" (no rain).\n",
    "\n",
    "![classification](http://ml4a.github.io/images/lin_classifier_2d.png)\n",
    "\n",
    "Classification is defined as the task of predicting the correct label or category of an unknown point. With two classes, we divide the data space into two halves, one for each class. So when we receive a new point, we simply find which side of the partition the point is in.\n",
    "\n",
    "![classification](http://ml4a.github.io/images/lin_classifier_2d_newpt.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k-nearest neighbors classification\n",
    "\n",
    "We will introduce a simple technique for classification called k-nearest neighbors classification (kNN). Before doing that, we are going to scale up our problem with a slightly more realistic dataset called [Iris](https://en.wikipedia.org/wiki/Iris_flower_data_set), which is commonly used to introduce data science tasks.\n",
    "\n",
    "Iris is a dataset containing 150 samples of flowers of the Iris genus, belonging to three different species (Iris setosa, Iris virginica, Iris versicolor). The dataset records their species (which is the class label), along with the following features: Petal Length, Petal Width, Sepal Length, and Sepal width. \n",
    "\n",
    "In the next cell, we import the dataset, and shuffle it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# load iris and grab our data and labels\n",
    "iris = load_iris()\n",
    "labels, data = iris.target, iris.data\n",
    "\n",
    "num_samples = len(labels)  # size of our dataset\n",
    "num_features = len(iris.feature_names)  # number of columns/variables\n",
    "\n",
    "# shuffle the dataset\n",
    "shuffle_order = np.random.permutation(num_samples)\n",
    "data = data[shuffle_order, :]\n",
    "labels = labels[shuffle_order]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view a table showing the first 20 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "<tr><td>class     </td><td>sepal length (cm)</td><td>sepal width (cm)</td><td>petal length (cm)</td><td>petal width (cm)</td></tr>\n",
       "<tr><td>versicolor</td><td>5.5              </td><td>2.5             </td><td>4.0              </td><td>1.3             </td></tr>\n",
       "<tr><td>versicolor</td><td>5.8              </td><td>2.7             </td><td>4.1              </td><td>1.0             </td></tr>\n",
       "<tr><td>setosa    </td><td>5.1              </td><td>3.8             </td><td>1.9              </td><td>0.4             </td></tr>\n",
       "<tr><td>setosa    </td><td>4.7              </td><td>3.2             </td><td>1.6              </td><td>0.2             </td></tr>\n",
       "<tr><td>setosa    </td><td>5.7              </td><td>4.4             </td><td>1.5              </td><td>0.4             </td></tr>\n",
       "<tr><td>setosa    </td><td>5.8              </td><td>4.0             </td><td>1.2              </td><td>0.2             </td></tr>\n",
       "<tr><td>virginica </td><td>6.2              </td><td>3.4             </td><td>5.4              </td><td>2.3             </td></tr>\n",
       "<tr><td>versicolor</td><td>5.8              </td><td>2.6             </td><td>4.0              </td><td>1.2             </td></tr>\n",
       "<tr><td>setosa    </td><td>5.2              </td><td>3.5             </td><td>1.5              </td><td>0.2             </td></tr>\n",
       "<tr><td>virginica </td><td>6.7              </td><td>3.0             </td><td>5.2              </td><td>2.3             </td></tr>\n",
       "<tr><td>versicolor</td><td>6.0              </td><td>2.2             </td><td>4.0              </td><td>1.0             </td></tr>\n",
       "<tr><td>versicolor</td><td>6.3              </td><td>3.3             </td><td>4.7              </td><td>1.6             </td></tr>\n",
       "<tr><td>versicolor</td><td>6.6              </td><td>3.0             </td><td>4.4              </td><td>1.4             </td></tr>\n",
       "<tr><td>virginica </td><td>7.4              </td><td>2.8             </td><td>6.1              </td><td>1.9             </td></tr>\n",
       "<tr><td>setosa    </td><td>4.8              </td><td>3.0             </td><td>1.4              </td><td>0.1             </td></tr>\n",
       "<tr><td>versicolor</td><td>6.6              </td><td>2.9             </td><td>4.6              </td><td>1.3             </td></tr>\n",
       "<tr><td>virginica </td><td>6.0              </td><td>2.2             </td><td>5.0              </td><td>1.5             </td></tr>\n",
       "<tr><td>setosa    </td><td>5.0              </td><td>3.6             </td><td>1.4              </td><td>0.2             </td></tr>\n",
       "<tr><td>virginica </td><td>6.1              </td><td>2.6             </td><td>5.6              </td><td>1.4             </td></tr>\n",
       "<tr><td>setosa    </td><td>5.2              </td><td>4.1             </td><td>1.5              </td><td>0.1             </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_names = np.array([iris.target_names[l] for l in labels])\n",
    "table_labels = np.array(['class']+iris.feature_names).reshape((1, 1+num_features))\n",
    "class_names = iris.target_names\n",
    "table_data = np.concatenate([np.array(label_names).reshape(num_samples, 1), data], axis=1)[0:20]\n",
    "\n",
    "# display table\n",
    "table_full = np.concatenate([table_labels, table_data], axis=0)\n",
    "display(HTML(tabulate.tabulate(table_full, tablefmt='html')))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, we will restrict our attention to just the first two features, sepal width and sepal length. Let's plot the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10aa4e240>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106260630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the original data\n",
    "x, y, lab = data[:, 0], data[:, 1], labels\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x, y, c=lab)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "plt.title('Iris dataset')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we are given a new point whose sepal length (`x`) and sepal width (`y`) are the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x, new_y = 6.5, 3.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot it on the graph. What could its class be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x10ac611d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bLo8r2eGpGjB58mSuvPJKunfvzh9//ME+++yT7JBUKeKfjey4EggBBiSANBqDLfWs+MYVP5J7Pfh+BeMECSHOAzCZr2Ns6TUSe1XpkbqqNVvXZUUk9F0W/rKUCS9/n4SIkuvFm95myW/L8RX5Kcovxl/s588pi3nj7veTHZqK0/bt2xk1ahQXXXQRjz76KJ999pkm9DpGpBjZMRokD6QQpADwQd59SHBlfGMXPBdO6PhKxi2GwCIk796aCH2PaFJXteaZq18rd9u793+UwEiST0T4YexUAr5ARLvfG+Db139KUlQqXiLCW2+9xUEHHUTTpk1ZtGgRp512WrLDUrH4JgMSY0MAKf40vrGLPgJ8ZRr94P0WkWB8Y+8hPf2uak3hzsJyt/mL/QmMJPksyyIYiP3H7dvLfhYNxdKlS7nyyispLCxk4sSJ9O3bN9khqYpYhUB0bX0IQdzX1qNXgdw9NiESmWr1SF3Vmn/cfGq52w47de+aOGS32znw8P2j2o0x9DmmZxIiUtXl9Xq59957GTRoEGeddRa//fabJvT6wH0ESCi63aRiPMfFN7ZrEDHTqeNAjHHHN/Ye0qSuas2gMwbQskPzqHaHy8FNr16RhIiS64YXLye1UQpOtxMAl8dJWpNUrn76kiRHpqrqxx9/pFevXixatIj58+dz3XXXYbfbkx2WqgJjbwNpo4EUYFedgFRwHVaSlOMYO+MOMI2AXQncBSYN0/g/cY1brVhEYl1jqLv69+8vs2eXvz62qnuev+ENvn3zZ6xgiH4nHMyd799ISkpi917riuzNO/jqpe9YNW8N3frvx7ArTiCzZeNkh6UqkZWVxS233MKUKVN47rnnOPXU8s9CqbpN/LOQok9AijEpw8B9HMbEv2MmVg5S9BEE5oFjf0zquRh76xqIOMwYM0dEKj3FqUldKaXKYVkWb775JnfddRcXXXQR9957L+npib1FSSmoelLXiXJKKRXD4sWLufLKK/H5fEyaNInevXsnOySlKqXX1JVSe72ff/6Z5cuXA1BcXMzdd9/N4MGDGTlyJNOnT9eEruoNTepKqb1aVlYWI0aMwOfz8f3339OzZ0+WLVvG/Pnzufrqq3UinKpX9Jq6SghvkY9QMERao9QaHdeyLApyC0lrlIrdUfGHr4hQuLMIV4oLV8kMdKUuvfRSXC4X+fn5TJ8+neeff56TTz452WEpFaGq19T1SF3Vqh1bc7lr2EOcnnkxZ7W4lKv738bqBWtrZOyvXprE2a0uY2Tb0ZzZ7BLGPvAx5e2kzp+yiEsOuIGzW13G6U0u4uELnqG4oLyCEaq+GzduHN26dcNut9OtWzfGjRsXs9/UqVP57LPP+OSTT2jTpg2TJk3i2GOPTXC0StUcPVJXtcayLC478EY2r9pGKPh30Ye0xqm8vfxZGjdvVO2xf3xvGk9e8TK+or9LM7pT3Zx/91mce8cZEX3XLd3I1f1vj+jrdDvpeVR3Hp10T7VjUHXTuHHjuOmmmxg6dCgdOnRg3bp1TJw4kSeffJLzzjsvom/z5s3Jzc2lUaNG5Ofn06JFC5588klGjBiRpOiVik2P1FXSzft5EdmbdkQkdICAP8iktybHNfa7930UkaQBfEU+PnzscywrshTk+CcnRNVcD/gCLPxlKRtXbI4rDlX3jBkzhqFDh7Lvvvtit9vZd999GTp0KGPGjInqO3bsWGbPns3SpUvxer1s2rRJE7qq1zSpq1qzZdVWxIo+E+Qv9rPhr01xjb19Y07M9uJ8b1QCX7dkA1Youuaz0+Vgy+ptccWh6p6VK1fSoUOHiLYOHTqwcmX0SlwnnXQSvXv3pmXLljohTjUImtRVrencu1PMNZE8aW66H9Y1rrE7Htg+Zntmq8ZRa5MfNHB/nK7okgx+b4BOPTpEtav6rXPnzqxbty6ibd26dXTu3DlJESmVOJrUVa3Z/5AuHDCgCy7P3zPN7Q47jZplcPS58dVavvyxC3GnRCZvd6qL0Y9fiDEmov2MG4bhTnNjbCai73EXHkWzNplxxaHqnjFjxjBx4kRWr15NKBRi9erVTJw4Mebpd6UaGp0op2qV3+vnvf+M59s3fyLgC3LE6Ydw6UPn10i98/lTFvH6neNYu3g9rTu1ZNQDIzm8nNXfNq3cwmt3jGXujwtIa5zKGdedzJk3DsNm0/3ahmjcuHGMGTOGlStX0rlzZ8aMGRM1SU6p+kRrvyullFINhM5+V0oppfYymtSVUkqpBkKTulJKKdVAaFJXSimlGghdTz3Jigu9jP/fV/w07hfsTjsnX34cw686sdLFSZKpILeQDx/7nGnjZ+JJc3P6tUM5YdQQnUmuVD0nVhFS9AYUTwDjgJRzMKnnYYymivpCZ78nUSgY4toBd7JuyQb83nAVNHeqi77H9eL+z29PcnSxeYt8XHHwLWRtyCbgCwLhYjJDRhzBLa9dleTolFLVJRJEsv8BwZXArhLMHnAPxJb5UjJDU+js93ph+pez2bh88+6EDuAr8jP3hwUsn7sqiZGV74d3p5KzJXd3QgfwFvr4adw0Nq/emsTIlFJx8f0IoTX8ndABvOCbgQQWJSkotac0qSfRn1MWUVzgjWoXy2LR9L+SEFHl/vjxT7yFvqh2u9PO0pkrkhCRUqomiH8WSFGMLRb45yY8HlU9mtSTqFXHFhElVHdxOO00a9s0CRFVrlWnljicsa/3N2urJVeVqrdsbQF3dLtxgL1lwsNR1aNJPYmOu/CoqAlxxhhcKW4OO6VvkqKq2ClXHI/dGTlpxma3kdmyMT0GHZCkqJRS8TKpp4Mpu8NuwHjAfXRSYlJ7TpN6EjVp0ZhHvruHVh1b4E514fI46dRjH56cej9OV/QRfF3QtnNr7vvs/2japgmeNDcuj5Nu/Tvz+E9jdPa7UvWYsTXFZL4FtnaAB3CDoyum6XsY46rk2aqu0NnvdYCIsGnlFhxOB606tkh2OFViWRabVmzBk+amebtmyQ5HKVVDRARC68A4MPZ2yQ5Hlajq7He9+bAOMMbQrkubZIexR2w2G+27tU12GEqpGmaMAUfHZIehqknPlyqllFINhCZ1pZRSqoHQpK6UUko1ELV2Td0Y4wGmEr7x0QF8IiL3lukzCngc2FjS9JyIvFZbMamaYVkWnz87kR/GTiElPYXz7z6Lvsf2Krf/zG/m8sEjn+H3+hl62XGcfPmx5c6Uz83ayQ/vTmXr2ix6DT6IgcP7J6UO/sr5a5j84a+IJQw+ZyBd++6X8BiUUmpP1drsd2OMAdJEpMAY4wR+AW4Qkd9K9RkF9BeRa6s6bkOc/V6fWJbFhftdw7Z12yPah15+LDe/fGVU//+c+yRTPpwe0bbP/m15bdGTUYl98W/LuOOEBwgFQ/i9AVLSPbTr2ob/Tb2flDRPzb+Ycox94GM+eORzAr4AArg8Ts68YRiXPnhewmJQSqnSkl77XcIKSh46S77q1/1zKsrY+z+OSugAE1/9kW3rsiLaVi9cF5XQAdb/tYkvnv82ok1EeOjcpygu8O6uhV9c4GXdkg2Mf3JCDb6Cim1Yvpn3H/4MX7EfyxLEEnxFfj596mvWLFqfsDiUUqo6avWaujHGboyZB2wDvheRmTG6nWWM+dMY84kxZp/ajEfF77t3ppS77fNnJ0Y8/uzpb8rt+82rP0Q83rRyC7lZO6P6+b0Bfnxv2h5GWX0zvpyNWNH7nkF/kOlfzEpYHEopVR21mtRFJCQivYH2wKHGmB5lunwFdBKRXsD3wNuxxjHGjDbGzDbGzM7KyorVRSWIw1n+NAyHO3Kb011BX1fkNofTQXlXgsqrNV8bHE47xmai2o3dltA4lFKqOhIy+11EcoGfgZPKtGeLyK4lv14D+pXz/FdEpL+I9G/Ron5UXGuozrhuaLnb/nHzqRGPR9x2Wrl9z7lleMTjVh1b0LZzq3Dhi1LcqS6GjT6+GpFWz5FnDYjZbrMZjjr78ITFoZRS1VFrSd0Y08IY06Tk3ynA8cDSMn1Kl1EbDiyprXhUzTjt2qEcOLBbVPs/HzmfRk0zItpadmjBef86K6pvvxMO5uhzB0W1//vjW2jcohGpGR5cHifuVDd9ju3JqVeeUHMvoBLN2zXjhpdG4/I48aS5d9e3v/rpS2jdSVeqUkrVbbU5+70X4dPpdsI7Dx+JyP3GmPuB2SLypTHmYcLJPAjkAFeJyNJyB0Vnv9cV8ycvYsLL35HWOJXz7/4HLdqXX/998+qtjHvwU3xFPk6/bigHHr5/uX0D/gC/TZhL9qYcDjy8G936da6N8Cu1Y9tOZnw5G0Q47NR+NG2ty8oqpZKnqrPfdUEXpZRSqo5L+i1tSimllEosTepKKaVUA6FJXSmllGogdD31OsDv9bPwl6XYnXZ6HHFAjdY6nzFhDsvnrOSwYX3p1r9LjY2bs2UH3775M+lN0jj58mNxOMp/K4VCIRZPX4bf6+egIw7Ak+out6+IsOrPtWRv2kHXfvuR2bJxjcW8de02fnh3Ks3aNeWEi4eUW39+b2CJMG/LZgr8fvq0bkOGu/zfyZ6S0DYILgZbO4yza8V9rUII/AEmBZx9MGbv/Z0oVRMqnShnjHEDZwGdKLUTICL312pk5WhoE+WmfzGLRy58ZnfBE4fTzgNf3lHhDPGq2LYui8t73UJRXvHutnZd2/DKgidwuZxxjf3EP1/g2zd+3v3Y2Ax3f3ATR/0j+j7uv2at4O5THg6XfjVghSxuff1qBp8zMKrvjq253Dn0QTYu34zdYcfvC3DaNScx+rELo+5f31P3nPYIv301Z/dju8POw9/+iz7H9Ixr3PpoRU42F38+njyfF2MMQcvi7iOHcF7Pg+MaV8RC8u6H4vFgnCBBcO6PyXwVY2sS1d8qGg9594FxAAImDZP5GsZ5QFxxKNUQ1djsd2PMt8BOYA4Q2tUuIk/EG2R1NKSkvm1dFpd2vxFfsT+iPbVRCh9seJmU9JRqjz1ynyvI3pgT1T7wtP7c99nt1R536iczeOCc/0W1G2OYUDgWl8e1u83v9TOi7WgKcgsj+rpTXLw8/7+069Imov3mIfeyePpfhIK732Z40tzc9PIVHHPekdWOefxTE3jp5uhihXannW+Kx+1VR+why2LQm6+yrbAgYiEGj8PB+2eN4OBWras9tlX4PuQ/AhSXanWC6whsTV+J6CuBpUj2OYA3chDTFNPyF4zRk4hKlVaTs9/bi8gIEXlMRJ7Y9VUDMe71vn93CqGQFdUuIuF7pKspd3tezIQOMPPrudUeF+Dd+z+O2S4ifFJm4ZWZX8+N+fpCwRCT3poc0Za9eQdLf18ekdABvIU+xj/1dVwxf/zEVzHbQ4EQP3/wa1xj1zezN22kwO+LWlnJHwoxbsH8+AYvepvIhA4QAP+viJUX0SpFHwJ+ovnA/1uMdqVUVVQlqU83xux95ygTIC+7gKA/GNUeCobI31EY4xlVk5+dX+62WEl2TxTuLCp3W+7W3IjHBbmFiBX9/YKBEHnbIz/kC3cWYbfHfjsW7CiI2V5V3gJvudvK2/lpqPJ8PgzRlzIsEbKLyv/dVomU976zgZQZW3YA5bwXreiFfZRSVVNuUjfGLDDG/AkMAuYaY/4qWU1tV7uK0yEn9cETc51wQ9/jqr8f1a5rG+yO2L/aVh3jq51/+Knln/056dJjIh73ProHVoydCE+6mwHDIsv8t+vaGleKK6qvw+Xg8OGHVDPasD7HlF1H6G/HnF/90/r1Ub+2bfFboaj2FIeDk7pUPKmtUu7BhAtIlmHLBFuriCbjPhZIje4rAXAdGl8cSu3FKjpSPwU4FRgKdAFOKHm8q13Fqe9xPek1uDuetL9nHnvS3Ay99Bj22b9dtce12Wxc/tiFUe3GwJ3vXl/tcQEuf+wCUtKjd0QOHnIQ+/XqFNHWZr9WDL/mpKjX131ANw49uU9EX7vdzi2vXoU71YWt5IjdleKiSYtGjLzj9Lhivv7Fy2OuGDdk5ECat20a19j1TdOUVG46bCApDsfu4/UUh4POTZtxarf4JqiZ9OvB1gRG6Rl0AAAgAElEQVTY9fu2AymYxg9FT3T0nATO/cOz3ndLgfQrMXZdtEmp6qrKRLl3ReTCytoSpSFNlIPw7V5TPprBD+9Owel2cvI/j+XQk/vGPdsbYPakebx0y9tkb9rBvr06cMOLo+nYvX3c4xYVFPPCDW8y/YtZuFNcnHnjMM4us+raLiLC7Enz+PrVH/AV+TjmvCM55txB5d62t3rBWj59+hu2rN5GvxMO5pQrjie9SVrcMeduz+PZa15j7g9/ktoolfPuOoNhlydu9be65veNG3j3z3nker2c3KUrZ3Y/CHcFtyVWlVg7kKL3wPc7ODpi0i7GOGLfSinih+KvEO8EMBmY1HMxbl0JT6lYanL2+1wR6VvqsR1YICIHxh/mnmtoSV0ppZSqTNyz340xdxpj8oFexpi8kq98YBvwRQ3GqpRSSqkaUG5SF5GHRSQDeFxEGpV8ZYhIMxG5M4ExKqWUUqoKyr2IZozZdcr941L/3k1E4rvhWSmllFI1qqKZMbsKzHiA/sB8wAC9gNmAzmjZywUDQYzNYLdXXqs+FAohluBwaqUwFSkQ9GIzduz2+MoXJ5JlFQJubDZ9P6u6paLT70eLyNHAZqCviPQXkX5AH2BjogJUdc/qBWu5fuC/ODnlPE5Ju4BHLnyGwp2xi+XkZefzwIj/MSz1fIalnM/Ng//N+r/07aNgVdZvLFl2DGT1IrClJ78vPo887/Zkh1Uhq+gTrC09YVsf2HYgVtZQLCu38icqlSBVmf2+SEQOqqwtUXT2e3LlbNnBJQfcELFQjNPtoEvvfXl6+oMRt+KJCKMPvoUNf20iGAgXPDHGkJ6ZxtvLnyUjMz3h8au6YXv+Wpy5w0hz+ClZywhfyMaawrbs3+WHOlmP3/JNhx2jojfYmmFrOSPh8ai9S03Wfv/TGPOaMWZIydergFaU20t9/coPUaVtA74gqxeuY9mcVRHtf05ZzNY1WbsTOoQTvd/r57u3JyciXFVHLd/wAk5baHdCB3DbLfZJ3cKKbVOTF1hF8h6M3W5lY/m0Xr2qG6qS1C8BFgE3lHwtLmlTe6HVC9aFl1Etw9hsbFy+OaJt4/LNiBV9JshX5GfNovW1FqOq+5ysxGOPLldriSGv6K8kRFQF1ubytwXmJS4OpSpQaVIXEa+IPCkiZ5R8PSki5a+QoRq0/Q/pHLNGuxUKsW+PfSLaOvXYhxhrh+BJc9OtX+faClHVAwHTneJg9CQzuxGaZsS3rnutsXcsf5trYOLiUKoCFRWf+ajk/wtKFnKJ+EpciKouGfrPY/GkurGVOm/q8jjpMag7+/aM/NDrflg39ju4I07337OabXYbqRkpHHfB3rWQiorUvcM1eEMOgtbf7yNv0M6Kgn3Zr8VhSYysAo3GxG63tcPm6pXQUJQqT0VH6jeU/H/XAi5lv9ReqFHTDJ77/WEOH34I7lQXGU3TOe3aodz/+W1RfY0xPDLpHoaNPo70Jml40twMOnMAz896hJT0lBijq71Fk9TW+Bp9wMKdB1EcdLDT72F+/rEc0PmjZIdWLpvrYGjyEpgmJS0GnIdC84lJjUup0qoy+/0yYKqILE9MSBXT2e9KKaX2NlWd/V6VygkdgJeNMZ2AOcBUYJqI6MwQpZRSqg6pykS5e0XkGOAgYBrwf4STu1JKKaXqkEqP1I0xdwNHAOnAH8CthJO7UkoppeqQqpx+PxMIAl8DU4AZIuKr1aiUUkoptccqTeoi0tcY04jw0frxwCvGmG0iMqjWo6tDVv25lvcf/pQ1C9fTpe++jLzjDDp2bx/3uH6vn69enMQP703D4XQwbPTxnHDx4JhlMi3L4rXbx/L1q+Gqbt0P68bt71xHi/bN4o5jTxQXFPPZsxOZ+tEMUjI8nHbNSQw+Z2BEiViVWL5gkLEL5vH50iU47XbO7dGLs7ofhK2B/E5CVogvFo7jvYUr8YUMw7s054J+F5LqahSzv/h/RwpeDReMcR2GSbscY2+V0JhFgkjRJ1D8MSCQciYmdQTGxF64RnxTkMI3wMoB92BM2qUYW9OYfZdsz+LFWTP5K3s7PVq24qr+h9KlafyfAyI+pGgcFH8BxolJGQkpZ2BM3Svbq2Kryuz3HsCRwGDCq7WtJzxR7t+1H160ZMx+XzBtCXcOfRC/149Ygs1uw+Vx8t+f72P//tUvohIKhbj5qH+zcv4afEV+IFyYZcCwftz9wU1R/a897E7++n1FRJvdaef99S+R2bJJVP/a4Pf6ubr/7WxetXV3ZTlPmpsTLzmaa5+5LCExqEghy2LEJx+weHsW3mC4hG+Kw8Ex+3bm2aGnJDm6mnHr1/9h4hoHxaFwQvTYg3RpXMwnI+/A5fBE9LWKxkPe/cCu9QkcYNIxzb/E2FsnJF4RQXKvAN/MUnF4wNUHk/lW1A6wVfg65D9Tqq8LbE0wzb/C2DIj+v6+cQOXfDEeXyiEJYLdGFx2O+POGsHBrar/+kRCSM55EFgC7KovlgLuo7FlPlXtcVXNqMna748AGcAzQPeS1duSktCT5dnrXsNX5Ntd8tQKWXgLfbx445txjTvz67msXrBud0IH8Bb6+G3CbFbOXxPRd8W81VEJHSAUCPH89fHFsSd+GvcLW9dmRZSK9Rb6mPjaj2xbl5WwONTffl6ziqXZ23cndIDiYJAfV69kSda2JEZWM1ZkzeObNc7dCR3AG3KwKs/Dd0s/j+grEoD8h/g7OQIEQQqQghcSEzBA4A/wl07oAF4IzAd/ZJ14sQog/+kyff1g5SKF70YNPWbyjxQHg1glB2QhEYqDQf4z9ef4YvZNhuBf/J3QCcfk+wkJLIlvbJUwVZn9foqIPCYi00Ukuuh3AxcKhVizYF3MbUtnRSfZPTHv54UUF0RX3BVLWDAt8o9o8ofTyx1n/uSFccWxJ2ZNmoe3MHpKhd1pZ9H0ZQmLQ/1txvr1FAWi/zRFhFmb6v8yt7PXz8UQfUaxKOjkl/UrIxtDa4HomvIQBP+vtRJfTP5ZIP7odilC/LMi24JLwMS6EuoH35SIlpBl8Vd27OVp52/dUs1gS0Lz/wZSFGsL+LU2SH2hF0oqYbPZ8KR5Ym7LyEyLa+xmbTJxeaKvr9mddpq2jjyd3ma/luWO07h57OuKtaFF+2bYHfaY2zJbNU5YHOpvrdLTcdujfycOm43mqfG9R+uC5mmZ2Ex0UnfaQrROS41stDUFCUb1DW9rUQvRlcPWHHDH2JCCsbeI7hszZgNlLhfYjCHVGfuafGN3rO+3B2wtiRmzcUDZmFWdpUm9EsYYhl99Iu4yi5i4U92ceeOwuMY+/qLB2OzRvwKny8lhp/SLaBt62bHYnbGT6agHRsYVx54YNvo4HK7IOIzNkJGZTq/BByYsDvW3Mw44EHuMiUxOu51j990vCRHVrKM6D8VjtzBYEe0OY3F2r8g5A8bWFFyHA2UWHTIpmLR/1nKkpXhOhFiTy4wNPCdHNjn2BUc3ouctezBpoyL7GsMFvXrjcUT2TXE4uKR35GfGnjIpp8eOGSe4j45rbJU4mtSrYNQDIxk8YiBOj5O0xqk43U5OuHgw5/zfaXGN27R1Jv/56k6atGpMSroHT6qbNvu14r8/3YvLE/mhZLPZeOKnMZFH9gbOuvkUBp0xIK449sQ++7fjX+/fREZmGikZHtypLjoe2J7//jQm5ox9VftapKXx2vAzaJ6aSprTSYrDQYfGjRl35jm4HVW5a7Vuczk8jDvjZDpmFJNiD5DmCNDU7eWlk3rSrknXqP6myf/AdSjgBpMOpEDa9RjPcQmL2djSMU3fBlsbMClgUsHWCpP5JsYWfUbLZL4Mzl5/x2zSoNHdGNchUX1vPuwITum6P267nQyXC5fdzpndD+KKftF99yhmewtMk1fCZw5MKpAC9n0wTd/FmDjPAqiEKXf2uzHmK4hxIauEiAyvraAqksza7zu357Fl9TbadG5Fo6YZNTauZVmsXrAOh8tBhwPaVXpr2Nwf/2TH1p0ccfoheFJjXxqobaFgiNUL1uFJ99C+a5ukxKAiWSL8tT0Lp91O58ymDe4WQ8uyWJX9J76gl/1b9sNhj30aehcJbQErC+ydMbbUCvvWFhGB4HJAwNGt0t+JBDeA5IKja6WJNKe4iPV5eXRs3JgmnppbIEnECk+YM87wz66BvY/qq6rOfq8oqQ+u6IkiMqWi7bVFF3RRSim1t4l7QZdkJW2llFJKVU9Var93BR4GDgR2n+sVkfo/A0cppZRqQKoys+lN4EXC9d+PBt4BxtZmUEoppZTac1WZGpsiIj8aY4yIrAXGGGPmAHtVVbnatHrBWn79YhYOp4PB5xxOm31rpkZ1caGXKR/NYMvqrXTr15kBw/qWe495cbGP/456niUzltFin2bc+uY17NOtbY3EoVRNkNBG8E5ELC/GcyzG2T3hMYRCIcbO/4pvlq2kaYqLGw8fyv4tu9TI2Dn5i1m24Rms0E4aZRzBge2vwhaj/oBSFalK7ffpwCDgE+AnYCPwiIjsX/vhRWtoE+Vev+s9Pnv6GwL+IDabwWa3cfXTlzDs8uPjGnfDsk3cMOhu/MV+vIU+UtI9tOrUgqemPUBa48iCJJtWbubibtdH3etw9dOXcsZ1Q+OKQ6maYBV9Cnn3AhbhinEuSB2BrdG/EhaDL+Bj8JtPsc0bmWhvG9CRKwecHdfYi9Y+T0f78xgEtz1EccjByryWHNTtG5yO5MzcV3VLTdZ+vwFIBa4H+gEXAhfHF54CWDZnJZ898w2+Yj9WyCIYCOH3BnjhhjfJ3rwjrrEfuehZ8rMLdpd0LS7wsnH5Zt657+OovjcPvjfmzYsv3PhGXDEoVRPEyilJ6D4gQDixe6H4I8Q/N2FxPDjl3ZKEbiK+Hpu5lkJfYbXHDQa9tOYlUh1BUhwhbAbSHEG6NNrGHysSt9OiGoaq1H6fJSIFQB5wvYicKSK/VfY8VbkpH8+IWBhlF2OzMXPCnGqPW5BbyIo/VlP2LEzAF+TnD6LrX2dvKmcHQmD+lEXVjkOpGuGbCsQ4DS1epPjrhIUxYUU24UQe7eOF31V73JVbPsBli65Xn+oIksbv1R5X7Z0qTerGmP7GmAXAn8ACY8x8Y0x89QgVADabiVnYwZhd/6keY8r76NnzYcu7Bq9U4lTwpq0jhVHiKdBS0VrlUtFrVyqGqpx+fwO4WkQ6iUgn4BrCM+JVnIaMOAKnK3quohWyOHx4pZdOypXWOI39D+2CzRb5geB0OznuguiaQi06NIs5jjHQ44gDqh2HUjXCPYTYK6+5MZ5TExbG6V1bEOs6lQHO6XlStcfdr9U5FIeiPweKgg6KbYdXe1y1d6pKUg+JyLRdD0TkF8K3t6k4dT64E+feeQYujxOny4ErxYXL4+TGl68gs2V8K57d/s51NGnZmJQMD3aHjZR0D/v27MAF//5HVN+nf30QY4s+Irj59avjikGpmmBsjaHxo4RXEPMAzvD/0y7BuA5OWBx3DrmI9mkhwon9769/D+pCirP65ZodDg85jhspCDgpDDoIWIaioIOlO9vSZ78Haih6tbeoyuz3p4AU4H3C7+IRgJeSe9VFJHEzVWh4s98BNq7YzIwvZ+NwORh05gCat21aI+P6fQGmf/47W9Zk0bXvvvQ5tme5i66EQiGevfY15v28iFYdW3D729fStHVmjcShVE2QUBZ4JwE+cB8TXt0sCT5dNImvli6haYqbmwYOp32TdjUybl7xepau/R+hUC5NGw9m//ajamRc1TDEXfu91EA/V7BZROSYPQ0uHg0xqSullFIVibv2+y4iogvpKqWUUvVAVWa/tzLGvG6MmVjy+EBjzGW1H5pSSiml9kRVJsq9BUwCdtUMXQbcWFsBKaWUUqp6qpLUm4vIR4TLOCEiQWLfX1IvrV2ygQXTllBcUFxp3+0bs5k/ZRE5W+Kr9pZIC35ZwqdPf836vzZW2jcvJ5/5UxaxedXWSvv6fQEW/rqUFfOii9zUVUWBAL9v3MCy7O01HvNPq1fy9vw/yCosqLTvloJ8Zm5YT1Zh9auQxRIIelm6+XuWbZmMZVX8JyoiSGAp4p+NiLfSsTfnLmHxhgnsKKz8fVRXWEWfYe18AMv/V6V9s4oKmblhPZvy8yrtW+jbye9rf+CvrXOwLKvCviIhxD8//CXJ+9gUqwDxz0KCqyvvKwHE/wcSWIBIxa+vrhDxI/45SGBxpX/b4ff+IsQ/FxF/giJMnKos6FJojGlGyQ2axpjDgJ2VPckY4wGmEr4PxQF8IiL3lunjJrzqWz8gGxghImv25AVU1/ZNOdw97GE2LN+Ew2kn6A/xz0fO5/TrTo7q6/cFePSiZ/ntq9k43U783gCDRwzk1teuqrPFWXK27ODynjeTlx1OMi/eFL6F7oU5j0bNgBcRXrtjLJ8/OxGn20nAH+Sggd24d/z/kdYouu70lI+m88TlL2KMwbIsMls14T9f3UmHA2pmFnBtGLdgPg9Om4zDZiNoWezTqDGvn3Ym7TIaxTXuwm1bOfvj9/GFwh/Y9035iRM7d+HFYadF9fUFg9zy3UR+XL0Sl92OLxRiWNf9efS4E3GUc1dCVf257hM6cB9tjYUBsta5yUt5iq6tjorqK8G1yI7LwdpKuFKbhWTchy01OuZCXy4rVp9P14xVpIgdd26IGWsHM+CAF8q9kyLZLP+fkHMOJcchUPwulq0ttpaTo/qGLIu7f/6Bz5Yuxm134A8FOapjJ5456RTcjuiPx/fmvMZDv2Vjt1mELEP79Am8cdp5tGvSNaqv+OciudfA7p0mF2Q+i3EdWnMvtgqsgpeh4DkwTpAg4uiGyXwZY4+uTyG+aUjuTYR/dgImDTJfwjh7JDTmPWEVfwN5/yJcMcACW3PIfBnj6BzVVwJLkR1XgOwkfExroPHjGE9C53vXqqr8Vd4MfAl0Nsb8SjgJX1eF5/mAY0TkYKA3cFLJDkFplwE7RKQL8CTwaJUjj9PdpzzM6oXr8BX5KdxZjK/Yz2t3jmPezwuj+r5+53v8NmEOfm+Awp1FBHwBpn0yg/ceHJ+ocPfY9Yf/a3dC32Xl/DU8fP7TUX2/e2syX74waffr8xf7WfjLUv57yQtRfdcuXs/jlzxPcb6XorxivAU+tqzaym3H3UcoVDdP4MzetJEHp02mOBgk3++nOBhk5Y4cLvlifNxH7KUT+i6TVq7gtbnRd2g8Pn0aP61ehS8UIt/vxx8KMXHFMp77Pb6qy9vyVtLZ9m8auXykOwOkOQO08BTQwncN3kB+RF8RC8kZBaG1IMUgBSBFkHcPElgcNfbilVfSLWMVHnuIRk4/bnuIno2m8vuKx+OKuVbljGB3Qt/F2oSVc0VU11fmzubLv5bgD4XI9/vwhUJMXbuGB6ZG3/Qza+1PPPRbNsUhBwUBF8UhJyvzUrnk83FRR+xi5SM7LgMrG6Sw5GsHsuNyxErcmT7x/gyFLwC+8O8aLwQXI7nXRvcNbUF2XAOSV/K+KARrG5Izqkpnc5JBgitg5x0lP9+S93JoPZJzcdSZERE/knMRWJvD/aQAJB/JvREJrk/SK6h5Van9PhcYDAwErgAOEpE/q/A8KakZD+FqEU6iyzGdBrxd8u9PgGNNPPUWq2jtkg1sWLYJKxT5h+gr8vHpU5G1pEWEb179AX+xv0xfP188921th1oteTn5bF2bFXPbtE9nRrV9/L8vdy/8skvAF2Tm13MozCuKaJ/w8vcE/JG1h0SgKN/L/J/rZp34t+fPxRuMjDkkwsa8fJZsj/1zqoqfVq+MSui7vDJnVsRjEeH9hQvwhiLj8AaDvPvnH9WOAWDFpjexmejTpDZjsWj9B5GNgbkguUT/KfqRovcjYwvk06PxPNz2yNeY6gjSzl43d2gt30zKvTronxLV9Pa8uRSXeW/4QiHGL1lMqEyifnveL3hDkR+ZltjYWOhmydYyNdq9EyHWqWsRSGC9eil8PbzzFiEIgYVIaHNk3+LPidoZAiAE3h9rK8S4SNEHhBf5iWgNJ3l/mZ1l35QYfQFCSHHdfD9XR7lJ3RhziDGmNey+jt4PeBB4whhTpeooxhi7MWYesA34XkTKZpR2wPpS32MnEHVOyBgz2hgz2xgzOyur+h/Cu+Rn52N3xr7ykLMlN+KxZVn4imJfdynKq/w6fDLszCr/umAoGP2Bl58T+9qusduiXuOOrTujdoYgfOIrLzs/qr0uyCoqirUIHQ6bIae4+r/DjRVcfy0MRL5nLBG8wVgfKFDgj++6nrGycdujfyd2YxEIZkc2WrnErqVugbUtoqXYn1du5fE0Z908ciO4qoKN0T+jfL8vRj8IWCECZeYlbC+2kBgfmQ6bsKMop8y3yiV2AvGBJHBOjpUTu904oOwZAysHiPFelGB037rC2k7MnTih5HdQum9u7B0tAuEzKg1ERUfqL1PyGzbGHAU8QvjU+07glaoMLiIhEekNtAcONcZU68KMiLwiIv1FpH+LFi2qM0SEzn32JRSIfiO4PE4OHx65Vo3dbqdzn04xxzlwYLe4Y6kN7bq2wWaP/att3i76Olrf43rG7J/eJI1mbSOryg04uS+eNHdU30AgSI9BdbNO/LGd9sNjj96J84cserVqXe1xh3bZv9xtfVq3iXhst9k4sEXLmH37tmkbs72qUtMHUxhwxthiaN20zLVCZx+QWMkmBVyRfRuntCHHlx7VM2gZVhXWzfc+FdaCjy693L9t+5g7LvtlZuJxRP5Mj+nYAo89ukK2P2SjV9sBkY2uAeFr2GWZFHCVvQpZizxDCJ8kjQoEHF0iW9wDwcRau92EX08dZNyDy4k5AK4y6465DiXmmQiTinEPqo3wkqKipG4XkV27eSOAV0RkvIjcA3Sp4HlRRCQX+Bkou+rBRmAfAGOMg/BfXa3vMqWkebj80fNxp/6dnFweJ5mtmnDaNUOj+l/33D/xpLl3Jz67w05Kuoern7qktkOtFpvNxsX3j4jeYOC2t66Jah51/0jSGqfuXlzG2AzuVDc3vjQ6ajLU4BEDad+tLe5U1+42T5qbM64/OeYOQ11wbs+DaZWejrtUYk9xOLj5sIE0ckfvoFRV89RUTu4SPUHKbgyPHHdiVPsDRx9HisOBveQKk8MYUp1O7jkqvvpOPdufzZrC9hQF/359RUEHC/P60al55KQsY28G6aPDyWU3DzjaY8pMlLPZbGQ77qA46CBohWP2h2wUBV20bjUmrphri82eDs5yPqCbPBnV9K8jB5PqdO2eqGg3hhSHg/8cfXxU33P7nEurFB/uUok9xR7gpv7pNEop89539gLXUWV+zinh5Ois/mJNe8qk/RNsmcCuv1cDeCDjHoxxRXZ2HQWOntExp5yMcUa/z+sEzzCwdyK8JsAuKZB6McYeucNuHB0h5awY7/0DwH1sAoJNjHLLxBpjFgK9RSRojFkKjBaRqbu2iUiFR93GmBZAQERyjTEpwHfAoyIyoVSfa4CeInKlMWYkcKaInFPRuDVZJnb+5EV8+vTX5GzJ5fBT+zH86pNIb5IWs++GZZv46L9fsmr+Grr168zZtw6nzX6taiSO2jL1kxm8evtYcrftpP3+bbnppdF06x97fyx78w7GPzmBBVMX06Zza865dThd+sSure0r9vHNaz8y+cPppGakMPzqEznslH5xLT9Z2/J9Pt5bMJ/vV62gaUoqo3r34Yh9OtbI2G/8MYeX58yiMOCnd6vWPHLcibRvFHtBnlU7cnh17myWbM+iZ8tWXN63Px0aN4k7hkDQy9zVL5BuTcQSB37XWfTpdGm5M9TFNwUpHBueBew+CZM6EmOLdcQDq7JmsH37szSyb2ZH8AA6t7+Vlo2iZxbXJVb+k1D4BuAH0xIyn8JW9sitxMa8PF6dO4t5W7fQrWkzRvc7hC5NY++g5nlzeG/uWL5fnUPTFMOogw9lUOfoAwEI386GdwJS9AkgmNQzwTOc8PFL4oi1Ayl8F3zTwN4ak3YJxtW3nJgDSNFn4P0cjBOTMgI8Q+v037aIFyn6CLxfg0nHpJ4P7qNjxiwi4PsufC1evOA5FZP6j+gdnDoo7trvxph/AScD24EOQF8REWNMF+BtETmikgB6EZ4EZyd8RuAjEbnfGHM/MFtEviy57e1doA+QA4wUkYouimntd6WUUnudGlnQpeQWtDbAdyJSWNLWDUhP9Opsu2hSV0optbepkQVdRCTqBloRWRZPYEoppZSqHXWzJJRSSiml9pgmdaWUUqqBSOw0zHrsu3cm89Y9H7B9Yw6tOrbgnw+fz+BzBiY7LJVkgVCIJ3+bznsL5lEYCNCrZSvGDDm23Pvfxy9ZxP9m/MqWgnzaN2rMbQOPZFi38u93rw3eYIBHfpnGJ0sW4gsGOaRtO+4bchxdm8WoBS7CO3/O4/lZv5FdVMS+mU25+8ghDOkU+86IaevW8J+pk1mRk03TlFSu7HcIl/aJfWeEBFcjefeBfyYYF3hOx2TcHnMWfp7Px4PTJvPVX0sJisWgfTpy35Bj2adx7LsM9sS3K5bxyK/TWL8zl5Zp6dww4HBG9ugV97gNnYgXyX8ciseD+MDZD9N4DMaxR3c8qxpW4US5uigZE+W+ffMnnrvuDXxFf1efcqe6uO2taznqH4cnNBZVt9w06RsmrVweUYY21enkq3MvZN8mkYV7Plm8kHsn/xhRltTjcPDf40/i5K6JS+yjPh/PzI3rd5e4NUCay8UPF15Cy7TIYjMvz/6dZ36fERXza6eewcB9OkT0nbVpAxd/Pj7iZ5HicHB530O48bDIHWCxcpCsE0Dy+btkrQucvbE1GxvZV4TTPnyPZdnb8ZfEbDOGJh4PP110WVy1Bn5ctZLrvp0QFfOdgwZzQa/e1R53b2DlXAr+WYSX+QAwYNIwzb/F2GMXWlLVV9WJcnr6vQreuueDiIQO4drvb/zr/XKeofYG2woL+HbFsqi68r5gkFfL1H4HeGLGL1qO808AACAASURBVFF1xr3BIP+d8Uutxlnaipxsft+0IaJmvQD+UIixf86P6Bu0LJ6fPTNmzE/EiPnJGdOjfhbFwSCvzp2Nr0y7FH0YPrqLKODrh8CCqIVlZm/eyKodObsTOoTL7hYHAny2NHoRmj3x+PRpMWN+6rfp9WZJ4WSQ4Arwz+bvhA7hmut+pGhcssJSaFKvVCgUIntT7LrHW1Zvi9mu9g5rcnNxxSg/+//t3XecXNV1wPHfmb5N2lVBddUBNQxICCREk2gWiCpTgwkYmxhsJxDjxDGxDXbi2NgG4woEHCA4GEwvohkDpoMkEOrNkkB9VxKrrdPeyR9vJHZnZqUtU3Zmz/fz0Ue7b+7eOXfezJyZ9+47N67Kspq2z42447C9nfXTN+058BrembJu9660y7xG4nGW7tjeZtunLS1E21mw5m+7U18Ta3e3VwxSqW1uuzAQ0eW0TQgJ4kmp375u1660CbY5FmNFTfdegx/vSb+KdF24JSXZm1Zif3Prx6eIQLRnLuzUW1hSPwCv10u/IVVpbxs0qvt16E3hGlVZSSSe+sbvFUmp8+71eDioLH21wu6u6d4ZY6v6EXNS618HvF4mH9Q25spQCL/Hm7afMVWpr4mxVe2t8yQMKEk6T+6fAKQ5bK4O+Ma07bdfv7Tn5Et8Pia0U0+/o0a0U/mvTzBEKM166ibBN8Zd6CVFAPyTch6O+Ywl9Q648j8ublMnHtxz6lf96NI8RWR6goPKyvn8uENS3vyDPh9XT52W0v6b02dSktQ25PPxrWNzt5jEuH79mTZ0GEHvZ8lacJN68jlkn8fDtdOOSRvzP89ILSj5zzNmpjwWJT4fX55yFMGk7VJ6MUiQtivGBcA/GfFPbNP2qCHDGFPVj0CrmD0ilPj9nDe+bdvO+taxx6eN+bpjZvTo0qj5Jr5xEDiKth/MBCSAlNr7Yj5ZUu+Az185m2/85ioGVvdHRBg8aiA33HOtTZIz3HLK6Vxx+BQqAgE8Ihw+aDD/d/6FKZPkAC6YdBg/OOkUhpRXIMDwij7ccsrpOZ0kB3Dn3HO4aNJhlPr9eEQ4ZthwHr3g0pRJcgD/MHUa/zLzBAaUliLAmMoqfnPGWWnr5k8bOpw7zzyHcf36IUC/khKunz6T645JfZ2Ipx/S/2EIzMCtJF0CJecjVf+d2laEB867gLMPGU/I6y6Ic/yIUTx+4d91a5IcwMljxnLraXMY0bcSAQaVlfHdE2bxxcOP7Fa/vYFU/RZKLkiskuYB/zSk/8M2SS7PbPa7McYY08PZ7HdjjDGml7GkbowxxhQJS+rGGGNMkbCkbowxxhQJuxDTmG7SyHto473g1EBwFlL6RcRTkbbtXzeu5+bXXmFbQz3Vffpy00mzmT58RNq26uxGG/8Xwq+DdzBS9iUkkPtZ2fPXrOYnb/6V2qZGxlb14z9nn8ph7dS213gN2nQvhN8D3wik7KqUS9T2tdUWtOlhaJkPUo6U/h0ET0p7KZnjONDwU2h+BIhD4CTo+wM8ntQZ+wCLt23l7g8WsnlPHceNGMnfHz6F/qWpNeWzyVHlqVUr+dPyJcRVuWDiZM49dALeNMV/egONrkIb74bYeggchZRdgXjTP4+yFoMqhF9Em/7oVjQMnYWUzkMkkNM4sslmvxvTDU7jH6D+FqA5sSUI3oFI/ycQT9uiMg8vW8K3X34xpY9fff5MzjxkfJtt6uxCa88G51Mggns9dxD63Iyn9LxsDCWt377/btoytg+cd0FK7XeNb0ZrzwNtBKK4BwIDSOXtSGhW27YaQXdeBLF1QEtiawmUXoanz7dS7s+pORPia9pulDIY+DYeT6jN5mdWr+Rf/vwC4VgMxb0GvyIQZP6llzOwnQJA2fD1+U/zyob1NMeiAJT4/BxbXc1dc8/tddfAa/gNdPe1uM9lB/CDlCD9H0N86T/UZoNTdzO0PAa69/VaAv4JSL8HkLQV8noOm/1uTJap05SU0AHCEK9Bmx5IaX/Tq39J2893/vJSat+Nd4OzG/dNENwa6S1Q/0NUIynts8FxHG575820t93w4nMp27T+dtA9uAkd3DfvFnTPd1FNqmLX8izE/8ZnCR2gGZruR+Pb2sYRfis1oYP74aHhV202xRyH773yMi2JhA5uCdy6cAu/ef+d9oaacYu3beWVDX/bl9ABmmNR3t70Ce9v2ZyzOHoCVUXr/h13X+99HkRBG9D6n+UujthG90iPtn69NkNsJYTTvzYLkSV1Y7oqthwkXRnVMLS0fZOIxGK0pCkpC1AfSZOkW17hs+TYmkJsbadD7YoNdbuJt3Mkb3tjQ+rGyJt89qbdirMHnLY12jX8WtKba4L4ILKw7bbmJ9sPMvxym183frqbiJNarz7mOLy6cX37/WTY25s+SVs3vzka5e1NH+csjh5Bd7unplI4EMndBy0i75E25WkTGn49d3FkmSV1Y7rKU9lO/WvAO6DNr+kWUdkr7YFYT+ra5oB7f570axFkWr/keu2teCXNeDyV7bSOgySd+/YMwK0kl0ZyP579rLGQ9Fj0CYXS1rYH6BfK3Tn1qlAIvzd1fEGvj6pQSc7i6BGklHae5eDJ3boHeCrdBYNS+Nt/vRUgS+rGdJH4xoFvNKnJKYSUXtFmi8fjYeLA9Mlp2tDhqX2XfQkk+c3f59ZG9w7pcsydURkqaXexmdPHHZy6sfRKIDnmgDv5LWlCm5ReDPiT2oqbAALT224u/yrtJoXy69v8OrC0jGlDh+FP+hC1twZ9rsw5+FA8ac6biwhnJc2fKHYiIQidBiRPRiuB0i/lLpDgiaQ+5wC8SMm83MWRZZbUjekGqboTfAcDJYlvoyGo+CYSnJ7S9sHzL2RwUn316j59uffc1DcUCc2GsmuB4Gf9+iYilb/Oyjja89iFl1AVajsRbfyAAdx++hkpbaVkHpReihtzhft/YArS98epbX3joO9/uZPdpNxN5t7hSL/7kaRTGh5POfS9jZS3q7Jr8QSPTun7l5+fy+cGDSbk81ERCBL0evnKlGnMSfdBJEv6BIPce+48+pWUUOb3U+4PUBUKcc/Z51FV0su+qQPS54cQOJo2z43SLyCll+QuBgkg/e4Dz2D3+SblIBVI5S8QX3XO4sg2m/1uTAZobK07sc03EfHsf4b14m1bWbh1C9OHD2fiwEH779eph9gK8AxAkpYkzaX3N29i6Y7tHD9iFOP67/9QpTq7IbYGPIMPOLNZNQzRJe6brG/CfmeFO44D4afBaYSSc/Ac4HFe/+ludjQ0MGHgQPoEQ/ttmy1xx2HJju2oKocNGrzf0zC9gcY2gbMFfOMQT3tL9WY5BlWILXMvafMfVjCXs3V09rsldWOMMaaHs0vajDHGmF7GkroxxhhTJCypG2OMMUWiZ9fFM71OczTKC+vWsL2xgSMHD2Xa0GE9vqRmfTjM8+vWsLu5mWOGV3N4O3XROysei/HcijtYum01laES5h1xLQMrRmak793NzTy/bg0NkTDHjxjF+AH7uRY8izS2FsKvuRPlQqfvd/KUxmsg/KI7wSk4C/GNzmGkxhQGmyhneoxVO2u55JGHiDpxwvE4Aa+XIwcP4Z6zzyeQppBHT/DB1i1c/sQjKG45Ur/Hw6xRY/jlnLlpr1PuqJZIA195/Lssqh1E1PEQ8MbxinLbrOHMHn95t2J+feMGvvqsW6Ut5jh4PR7OHz+RH846JWcfoFQVrf8RND0ExHG/X2jaOvEATvN8qPtX3OvVHff/sqvwVFyXk3iNyTebKGcKztfnP82n4RYao1FijkNTNMrCrVu4f/EH+Q4tLUeVrz77JI3RKE2JmJtjMV7ZsJ6nVq3oVt93vPk9FtYOojnuJ6ZemmIB6qMBbnpjHfFYO1XsOiAci/G1556mORajORYj6ji0xGI8sXIFr23c0K2YOyXyLjQ9jFsPPIpbP78Frbveranfijp1iYQeTrSPuD83/h6NfpS7mI0pAJbUTY+waU8dm+v3pGxvicX40/KleYjowJbu2E5TNLU+e3MsykPLlnSr75c+CdIST624tjNcwnsbn+hyv+9s+iTt9qZYlEdXLOtyv52lLY/TdiGcvTyJGvKthF8jfUnZMNr8VOaDM6aAWVI3PYKzn9NA+7stn/Z36qq7Mau2fxg8nrziWSc49JDHeb/3lTy+/Yy3G4+FMcXIkrrpEar79OWgNGtdh7w+5k2YlIeIDmzyQYMIeFPnmpb4fHxh4uRu9T1reCMhb+pRgL6BMDNGnd/lfqcPq06bvEv9fs4bP6HL/XaWlJxFap143AVrAjPbbguegHvePbmTEFIyNxvhGVOwLKmbHkFE+PWcsygPBCjxuYmy1O9n4sCBXHnElDxHl57X4+G3Z5xFqd9PMDGRr9Tv5+hh1Zw3fmK3+r5m5k1Mrqql1BcBlJA3SpkvwndnHITX1/WLVkr8fn5x+hmEfD6CXi8ClPj8nDpmHCePHtutmDslcByUnImb2D24i32EoO9PUhd/8fSDPjcBQdwFOTxu25ILkUDPfG4Yky82+930KHvCYZ5ZvZJtDfVMHTKM40eO6tYs8lzY1dzE06tXsqupmRnVIzhm2PCMzCKPxSI8tvg2lu/4hMpQgHmHf5nqft37sLDXjsYGnl69ivpwCyeOHM0Rg4fk5dJBjX6EtryGeEohdCbibf9yQI1tgvDzqNOChGYj/sw8FsYUAqv9bowxxhQJu6TNGGOM6WUsqRtjjDFFwpK6McYYUyQsqRuThqqyemctq3bWZvz67V3NTXy0fRuftqQrvtJWczTKkh3b2ZKmME8uabwGjS5BnYa8xmFMd6kqGluHRlegmuZSyQJnC7oYk2TJju1c8+yT7G5uQQQqAkF+e8ZZHDlkaLf6jcbj3PiXl3hq9UoCXi+ReJwLJ07m+yfOxutJ/Xx93+JF3PLmG3g9QjQeZ8qQofzmjLOoDKW5vjtLVFvQT78F4VdAAqBRtOxKpPz6Hr/QjjHJNLYO3X0NxLeBJC6NrPw5Epx5wL8tFPZN3ZhWGiIRLnvsYbbU19Mcc2u6b29s4PInHqWupaVbfd/2zls8s2YVkXichkiESDzOoyuWcefC91Pa/nXjBm5583WaY1EaIhHC8TgLtmzm6/Of6VYMnaV7bobwq0AEtAG35vp9aPMjOY3DmO5SjaK7LoP4RqAFtAl0F7r7WjS+Nd/hZYwldWNaeW7tauJpDrc76vDMmlVd7ldV+d+PPqAlaTGW5liM//lwYUr7uxa9T3NS26jjsHDrZrY11Hc5js5QbYHmp3EXUmmtGRrvzkkMxmRM+HV32d6UUskxtKl4PqRaUjemldqmRsJpVkFrjsWobWrscr+OatrFXwDqwslJE7Y3pD937fd42dnUlPa2jNP93I+zKzcxGJMpTi2kPYceBWdbzsPJFkvqxrQybehwgmnquZf6/UwbOrzL/Xo9Hg7tPyDtbYcPSq2idsLIUfjTnGdXlHH9+nc5jk6RKvBUpbsBAtNyE4MxmRKYSvrFgUqR5PUGCpgldWNamTpkKEcPH76v/jy4C7QcMWgIM4ZXd6vvm046mZDPt6/srVeEEp+Pfz9hVkrbr049mopgsE1iL/H5+PZxJxLsRu33zhARpM/NQAjYOynOC1KGVHwzJzEYkyniGwuhM0BaTzQNgW80hE7NW1yZZmVijUkScxweWraEPy1bggNcMGESF03+HAFvujW9O2dFbQ2/e/9dVu2sZdLAg7h22jHtfvOuaWzkrkXv8/rGDQwqr+ArU47iuBEjux1DZ2lkMdp4J8Q2QuBIpOxqxDci53EY012qDrQ8iTY96J5fD52FlP0dIrm7oqSrrPa7McYYUySs9rsxxhjTy1hSN8YYY4qEJXVjjDGmSFhSN8YYY4pE1q6NEZFq4H5gEG4Jn7tU9fakNicBTwLrE5seU9UfZCsm0z6NLEDrfwzRVeDpD+XXIiUXdLu+d1M0ys/eeoPHVi4jFneYNXo0Nx5/EoPLKzIUeebVtbTw4zf/yrNrVqEKnx83ju8cdxJVJakzZOOOw50L3+fexYtoiESYMmQo3z1hVrvXpGvLC2j9rRDfAr4RSPkNSCj1krbe4JNdq/iP1/7IG1uCBL0OFxzs5/oTriXkL8t3aO3S2Fp0z39B5H2QUii7DCn7KiK2jIbpGbI2+11EhgBDVHWRiFQAC4FzVXV5qzYnATeo6tyO9muz3zNPI4vRXV8EWtc2L4Hyr+Epv7rr/apywSN/ZOmO7UTibiUnrwj9S0p5+fIvURYIdC/wLIg7DnP+cB8b6z4l6riFKvweD0Mr+vDiZVfgT7qs7d9efpEnV61oU/61zO9n/qV/T3Xfvm3aOs1PQ92NtH2cQ0jlL5DQ7GwNqUfa3bSdU+6/h7qoH0fdA4ZBT4xpg5q5/4Kb8htcOzS+Fa09E7SRz0qNhiB0Gp7Kn+UzNNML5H32u6puVdVFiZ/rgRXAsGzdn+k6bbiNtokG3Prev0M10uV+P9i2lZW1NfsSOkBclfpIhCdWrehyv9n06sb1bG2o35fQwa25XtvUyJ/Xr2vTtqapkcdXLk+p5x6Ox/nvRamLtFD/M1If5xa0/qcZir5w/PHDh2mOe/cldICw42PBjhJWbuuZH9q18b40tcNboOUFNF48ZUZNYcvJOXURGQUcCbyb5uYZIrJYRJ4TkUm5iMckia1Ov10dt15yF63eWUu6I0HNsShLtvfMN8FVtbUpSRqgMRplVW3bx2L97t1pS8rGHIfFSeNTjYPTzkpQ8Y1dD7hALdmxm5Z46mPnFYdVNe08H/MtuhhIU79fAhBbm/NwjEkn60ldRMqBR4HrVHVP0s2LgJGqejjwK+CJdvq4WkQWiMiCmpqa7AbcG3n3U6XM0/U646Mqq/aVRG0t5PNxSDvnnPNtVGUVoTRlWEv9fkZVtq2DXt2nL5F46gcAr0jKOXURb/uPpTe19nuxO6RfBQFP6mPnqDCqXw+tVuc7lLTTkDQC3h4as+l1sprURcSPm9D/oKqPJd+uqntUtSHx83zALyIp7/aqepeqHqWqRw0cODCbIfdKUv6PuPW9WyuB0ssQCXa532OGDWdYn75t6pcLEPR6mTehZx6UOWXMWPoEQ3hbfRjxilAeCDBn3MFt2g6pqGD26DEp39YDXi9XT02z4EnZN5LqTgOEoOyfMhV+wbj0iHkEPA6tD2UHPDEOqWzhsCHH5i+w/ZCyK0H8SVuDEJxhZXNNj5G1pC7utOl7gBWqems7bQYn2iEiRyfi2ZmtmEx6EpwBfX8O3uGAB6Qcyr/c7UU7RIQHz7+QU8eMw+fx4BFh2tDhPHrhpfQNJX+I6BkCXi+PXngJJ4wchVcErwjHVo/k0QsvTbuQyq2nncFFkybvW6jlkP79uffceWnruUvpJVB+g7v6GR7wDIA+38NTek4ORtazHNRnBA+ffwpHDmjAg4PfE2fOyCj3nf8NPGlWp+sJxDcSqboPfBNw36qCUHIeUvnLfIdmzD7ZnP1+HPA6sITP1rv7DjACQFXvEJGvA9cAMaAZ+GdVfWt//drs9+xyJ8b5u30pW7K44+Copswe78lijoN2MGZHlWg83qEV1NzXXAQIZPxxLkThWDNe8eHzJn8L7rnc14kPkZ75AcQUn47Ofs/axZWq+gafrdfYXptfA7/OVgym80Syc5mZ1+OhcNK5y9eJb4wekQ4vieom8q6f1ig2QV/PXyErWbZeJ8Z0l33MNMYYY4qEJXVjjDGmSFhSN8YYY4qEFSw2AOxubuaBJR/yzqZPGF1ZxZVHTGFsmhncvYHjOPz07Td4dPkyFDj30An863EndOocuzHG5EPWZr9ni81+z7xtDfWc9eADNETChONxvCIEvF7umHsOx48Yle/wcm7WvXezcU9dm21Dyit4/Yov99jLrYwxxS3vtd9N4fjFO2/xaUsz4USN9rgqzbEY3/7zi2nLvBazJ1cuT0noAFsb6vnT8qV5iMgYYzrOkrrh1Q3riadJ3ruam9jW0JCHiPLniZXtLzTz5KqVOYzEGGM6z5K6oSKY/pppR5WyQOEUBMmEyjRrpu/VU6vgGWPMXpbUDVccMYWSpMIpfo+H40aMpE+wdyWy646Z0e5t10/vmTXJjTFmL0vqhksmf47zJ0wi6PVSEQhQ4vMx6aBB/Py0OfkOLedGVlbxb8edmLL9mzNm9tiV5YwxZi+b/W722d7QwPLaHQyt6JOydGhv0xCJ8ODSj1B1uGTy56joZUcsjDE9S95rv5vCM6i8nEHl5fkOo0coDwT4ypQDvn6MMaZHscPvxhhjTJGwpG6MMcYUCUvqxhhjTJGwc+pZsm7xBpa+sZJ+gys5Zu5UAsHiut77k7o6/vrxBkp9fk4ZM7bda90LVW1TEy+vXweqzB49loFlZfkOqSgV+/PImFyz2e8ZFo/H+dElt/Pu/IWoA16/F3/Qx62v3szIidX5Di8jfv72G9y9aAEigkcEFO6Yew7HjRiZ79Ay4rEVy7jxLy/hFQ+K4qjy/RNnc/Hkz+U7tKJS7M8jYzLJar/nyQu/f4X3nltEuClCpCVCc30z9Tvr+f55Py2KOurvbd7E7z9YSDgepyUWoykapSkW5Zpnn6QlFs13eN22raGeG//yEuF4nKZYlOZYjHA8zs2vvcKmNDXhTdcU+/PImHyxpJ5hz971Ei2N4TbbVKF28y42r9map6gy59EVy2iJxVK2C8IbH2/MQ0SZ9fzaNWm3O+owf83qHEdTvNp9HklxPI+MyRdL6hkWi8bTbhePEI2kvokVmkg8TnvHG6KOk9NYsiHmODhpjqg4qkUxvp4i2t7zSIvjeWRMvlhSz7CTLzuBYEkgZXtpRQkjJw7PQ0SZddYh4yn1p076izkOM6sL/1zoyWPG4pHUl4Xf6+XUMWPzEFFxmlvkzyNj8sWSeoad87XTGX3YCELlblnRQMhPqCzIjQ9eh8dT+A/3rFGjmT1qDKU+PwL4PB5CPh//MfsU+hTBzOXRlVVcc9TRhHw+PAgeIOTzceXhU6z2ewbNGjWa2aPHUOovzueRMflis9+zIB6L884zC/nwlaUMGN6PU794Iv0GV+U7rIxRVd7bvIk/r19HmT/AueMnMKqyeMYHsLxmB8+sXoWqcsYhh3LYQYPyHVLR6Q3PI2MypaOz3y2pG2OMMT2cXdJmjDHG9DKW1I0xxpgiYUndGGOMKRKW1I3ppWrq1/Pxzg9xnPS1FboqHIuxdtdO6lpaMtqvMebAbEEXY3qZmvr11Gz5CqPLN1Oiws6GAFvkOxw+4sJu933PogXc9u5bCG4RmTljD+bHp5xO0GdvNcbkgr3SjOlFHMehYfuljKvYhc/jXvlS6otRGruJj3eOZUT/qV3ue/6a1dz6zps0tyr/+vy6NXg9Hn522pxux26MOTA7/G5ML7J2x6sMCO3Zl9D38nnibN5+Z7f6/t2Cd9skdIBwPM4za1bRGIl0q29jTMdYUjemF2ls2US60hR+jxKSbd3qe0djQ9rtHhHqwnZ+3ZhcsKRuTC8ytOpY/J7UBVOaYz5avNO61ffUIcPcddGThHw+BpWVd6tvY0zHWFI3phcZ1Hcci+tm0hT7bDpNOO6hLlrK5BHXdqvvb86YSYnPh7dVYi/x+fj340/CWwTrHhhTCOyVZkwvc/T4u1gRvoa19YPZ3FjJh3WnUHrQ01SE+ner37H9+vPUJV9k7iHjqe7Tl+nDqrnrrHM5f8KkDEVujDkQq/1ujDHG9HBW+90YY4zpZSypG2OMMUXCkroxxhhTJCypG2OMMUXCkroxxhhTJCypG2OMMUXCkroxxhhTJCypG2OMMUXCkroxxhhTJCypG2OMMUXCkroxxhhTJCypG2OMMUXCkroxxhhTJCypG2OMMUXCkroxxhhTJCypG2OMMUXCl62ORaQauB8YBChwl6rentRGgNuBM4Am4ApVXZStmExmxByH59eu5rm1qykPBLl40mEcOWRovsMyxpheL2tJHYgB31TVRSJSASwUkZdUdXmrNnOAgxP/jgF+l/jf9FAxx+GKJx7lw+1baYpG8YjwzOqVXHfMsXxl6rR8h2eMMb1a1g6/q+rWvd+6VbUeWAEMS2p2DnC/ut4BKkVkSLZiMt334ro1fLjNTegAjirNsRg/f+dNdjY15Tk6Y4zp3XJyTl1ERgFHAu8m3TQM+KTV75tITfymB3l+7RqaYtGU7QGPl3c2fZLmL4wxxuRK1pO6iJQDjwLXqeqeLvZxtYgsEJEFNTU1mQ3QdEqfYAiPSOoNAmWBQO4DMsYYs09Wk7qI+HET+h9U9bE0TTYD1a1+H57Y1oaq3qWqR6nqUQMHDsxOsKZDLpp8GAGvN2W7TzwcWz0iDxEZY4zZK2tJPTGz/R5ghare2k6zp4DLxTUdqFPVrdmKyXTfYQcN4tszTyDo9VLuD1DuD1AZCnHvufPSJntjjDG5k83Z7zOBLwJLROTDxLbvACMAVPUOYD7u5WxrcS9puzKL8ZgMufzwIzn70PG8s2kTZX4/04dX47eEbowxeZe1pK6qbwBpTr62aaPA17IVg8meylAJnx93cL7DMMYY04pVlDPGGGOKhCV1Y4wxpkhYUjfGGGOKhCV1Y4wxpkhYUjfGGGOKhCV1Y4wxpkhYUjfGGGOKhCV1Y4wxpkhYUjfGGGOKhCV1Y4wxpkhYUjfGGGOKhLjl1wuHiNQAG/MdRycMAGrzHUQW2fgKm42vsBX7+KD4x9jR8Y1U1QOuPV5wSb3QiMgCVT0q33Fki42vsNn4Cluxjw+Kf4yZHp8dfjfGGGOKhCV1Y4wxpkhYUs++u/IdQJbZ+Aqbja+wFfv4oPjHmNHx2Tl1Y4wxpkjYN3VjjDGmSFhSzxAR8YrIByLyTJrbrhCRGhH5MPHvy/mIsTtEZIOILEnEvyDN7SIivxSRtSLykYhMyUecXdWB8Z0kInWt9uH38hFnV4lIpYg8IiIrRWSFiMxI9tRxEwAABu1JREFUur3Q99+Bxlew+09EDm0V94ciskdErktqU7D7r4PjK9j9t5eIXC8iy0RkqYg8KCKhpNuDIvJQYh++KyKjunI/vkwEawD4J2AF0Ked2x9S1a/nMJ5smKWq7V1POQc4OPHvGOB3if8Lyf7GB/C6qs7NWTSZdTvwvKp+QUQCQGnS7YW+/w40PijQ/aeqq4AjwP3yAGwGHk9qVrD7r4PjgwLdfwAiMgz4R2CiqjaLyMPAxcC9rZpdBexW1XEicjHwE+Cizt6XfVPPABEZDpwJ3J3vWPLoHOB+db0DVIrIkHwHZUBE+gInAPcAqGpEVT9Nalaw+6+D4ysWJwPrVDW5AFfB7r8k7Y2vGPiAEhHx4X7o3JJ0+znAfYmfHwFOFhHp7J1YUs+MXwD/Ajj7aTMvcVjsERGpzlFcmaTAiyKyUESuTnP7MOCTVr9vSmwrFAcaH8AMEVksIs+JyKRcBtdNo4Ea4H8Sp4juFpGypDaFvP86Mj4o3P3X2sXAg2m2F/L+a6298UEB7z9V3Qz8DPgY2ArUqeqLSc327UNVjQF1QP/O3pcl9W4SkbnADlVduJ9mTwOjVPVzwEt89mmskBynqlNwD/N9TUROyHdAGXag8S3CLdN4OPAr4IlcB9gNPmAK8DtVPRJoBL6d35AyqiPjK+T9B0DitMLZwJ/yHUs2HGB8Bb3/RKQK95v4aGAoUCYil2Xjviypd99M4GwR2QD8EZgtIg+0bqCqO1U1nPj1bmBqbkPsvsQnTVR1B+75rqOTmmwGWh+BGJ7YVhAOND5V3aOqDYmf5wN+ERmQ80C7ZhOwSVXfTfz+CG4SbK2Q998Bx1fg+2+vOcAiVd2e5rZC3n97tTu+Ith/pwDrVbVGVaPAY8CxSW327cPEIfq+wM7O3pEl9W5S1X9T1eGqOgr30NFfVLXNJ7Ckc1tn406oKxgiUiYiFXt/Bk4DliY1ewq4PDELdzru4aWtOQ61SzoyPhEZvPf8logcjfva6fQLLh9UdRvwiYgcmth0MrA8qVnB7r+OjK+Q918rl9D+oemC3X+ttDu+Ith/HwPTRaQ0MY6TSc0DTwF/n/j5C7i5pNOFZGz2e5aIyA+ABar6FPCPInI2EAN2AVfkM7YuGAQ8nnhN+YD/U9XnReSrAKp6BzAfOANYCzQBV+Yp1q7oyPi+AFwjIjGgGbi4Ky+4PPoG8IfEIc6/AVcW0f6DA4+voPdf4sPmqcA/tNpWNPuvA+Mr6P2nqu+KyCO4pxFiwAfAXUl54h7gf0VkLW6euLgr92UV5YwxxpgiYYffjTHGmCJhSd0YY4wpEpbUjTHGmCJhSd0YY4wpEpbUjTHGmCJhSd2YAiMiNyZWe/oosWJVRhfuEHdFrHSrDabdnoH7O1dEJrb6/VUROSrT92NMb2DXqRtTQMRdUnQuMEVVw4mqWoE8h9Vd5wLPkFoQxxjTSfZN3ZjCMgSo3Vt2WFVrVXULgIhMFZHXEovSvLC3kmHim+/tiW/1SxMVuRCRo0Xk7cQiKG+1qsh2QIkqfL8XkfcSf39OYvsVIvKYiDwvImtE5JZWf3OViKxO/M1/i8ivReRY3CqLP03ENzbR/IJEu9UicnwmHjhjegNL6sYUlheB6kSy+62InAggIn7chS6+oKpTgd8D/9nq70pV9Qjg2sRtACuB4xOLoHwP+FEn4rgRt4zl0cAs3KS8d2W0I3DXgT4MuEhEqkVkKPBdYDruegnjAVT1LdzymN9S1SNUdV2iD1+i7+uA73ciLmN6NTv8bkwBUdUGEZkKHI+bTB8SkW8DC4DJwEuJcrde3CUe93ow8fd/FZE+IlIJVAD3icjBuEvP+jsRymm4CxndkPg9BIxI/PyyqtYBiMhyYCQwAHhNVXcltv8JOGQ//T+W+H8hMKoTcRnTq1lSN6bAqGoceBV4VUSW4C4CsRBYpqoz2vuzNL//EHhFVc8TkVGJPjtKgHmquqrNRnfSXrjVpjhde5/Z20dX/96YXskOvxtTQETk0MQ3672OADYCq4CBiYl0iIhfRCa1andRYvtxuCt41eEu7bh3ec4rOhnKC8A3Wq2cdeQB2r8PnCgiVeIuKzmv1W31uEcNjDHdZEndmMJSjnvIfLmIfARMBG5S1QjuSlY/EZHFwIe0Xa+5RUQ+AO4ArkpsuwX4r8T2zn4b/iHu4fqPRGRZ4vd2Jdar/xHwHvAmsAGoS9z8R+BbiQl3Y9P3YIzpCFulzZgiJyKvAjeo6oI8x1GemBPgAx4Hfq+qj+czJmOKjX1TN8bkyk0i8iGwFFgPPJHneIwpOvZN3RhjjCkS9k3dGGOMKRKW1I0xxpgiYUndGGOMKRKW1I0xxpgiYUndGGOMKRKW1I0xxpgi8f+ppfnGXbk9RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106260fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the original data\n",
    "x, y, lab = data[:, 0], data[:, 1], labels\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x, y, c=lab)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "plt.title('Iris dataset')\n",
    "\n",
    "# put the new point on top\n",
    "plt.scatter(new_x, new_y, c='grey', cmap=None, edgecolor='k')\n",
    "plt.annotate('?', (new_x+0.45, new_y+0.25), fontsize=20, horizontalalignment='center', verticalalignment='center')\n",
    "plt.annotate(\"\", xytext=(new_x+0.4, new_y+0.2), xy=(new_x+0.05, new_y), arrowprops=dict(arrowstyle=\"->\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our simple approach to predicting the new point's label is to find the point in the dataset which is closest to the new point, and copying its label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 2\n"
     ]
    }
   ],
   "source": [
    "# calculate the distance between the new point and each of the points in our labeled dataset\n",
    "distances = np.sum((data[:,0:2] - [new_x, new_y])**2, axis=1)\n",
    "\n",
    "# find the index of the point whose distance is lowest\n",
    "closest_point = np.argmin(distances)\n",
    "\n",
    "# take its label\n",
    "new_label = labels[closest_point]\n",
    "\n",
    "print('Predicted label: %d'%new_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! That is k-nearest neighbors where we set `k = 1`. If `k > 1`, we find the `k` closest points and take a vote among them.\n",
    "\n",
    "We can now plot the newly-labeled point on top of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x10ad9fe80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac614e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# append the newly labeled point in our dataset\n",
    "x = np.append(x, new_x)\n",
    "y = np.append(y, new_y)\n",
    "lab = np.append(lab, new_label)\n",
    "\n",
    "# scatter plot as before\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x, y, c=lab)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "plt.title('Iris dataset')\n",
    "plt.annotate(\"\", xytext=(x[closest_point]+0.02, y[closest_point]+0.02), xy=(new_x-0.02, new_y-0.02), arrowprops=dict(arrowstyle=\"->\"))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
